Assessing the Risk of DEI Roll-Backs
Caught between a rock and a hard place! Or maybe not.
Background
Just a few days ago, I was invited as a guest speaker on a US-based DEI conference hosted by Joe Santana (he writes on Medium too). Joe reached out to me a few months back to see if I would be interested in getting onto a podcast to discuss about DEI measurements. This was predicated on the DEI article that I had published on Medium last year, which has now garnered over 42K views. While that podcast recording is scheduled for an end May release, for now, I want to share about a construct that I had put forward in the said recent DEI conference.
Joe has posed this question to me: “How should US-based organisations evaluate their response to the recent anti-DEI executive orders from the government?” While I’m not based in the US and don’t have a front-row seat to the seeming de-evolution of DEI, I did spend some time thinking about the question. And so I dedicate my 86th article to the framework that I had responded with on how to assess the risks of DEI rollback.
(I write a weekly series of articles where I call out bad thinking and bad practices in data analytics / data science which you can find here.)
The Risk of DEI Rollback
The recent anti-DEI executive order has resulted in a number of organisations making poor decisions. Well-known faux pas such as Target are often cited. Target publicly announced it’s roll-back of their DEI policies, to quickly align with the new anti-DEI executive order, and its customers have punished them by reducing their spent, leading to a severe drop in their stock price. Now add on the tariff wars that have just erupted, and Target finds itself caught between a rock and a hard place. Another rollback shocker was Accenture, which was seen as an industry leader for DEI advocacy. In fact, their pro-DEI positioning is arguably part of their recruitment value proposition. Their recent rollback has allegedly led to senior talent resigning. And they’ve been dealt a second blow by DOGE (US government downsizing initiative led by Elon Musk) with government contracts likely to be reduced significantly (along with the likes of McKinsey and others). Now, the majority of Accenture staff are based outside the US, and one should legitimately ask if Accenture could have reacted more strategically to the anti-DEI executive order.
These examples provide the background context to why Joe had asked me the question on how US-based companies should be navigating in the current government-led anti-DEI climate.
Risk Framework for DEI Rollback
I gave Joe’s question a lot of thought, and presented at his DEI conference this framework: the risk of DEI rollback should be assessed across 4 dimensional categories:
- Top-line risk
- Policy risk
- Internal risk
- Community risk
Let’s unpack each one in turn.
Top-Line Risk
Top-line risk refer to the obvious perceived risk of not complying to the anti-DEI executive order — essentially, what actions will the government threaten to take? To unpack this risk, we need to understand if the (US) government has agency on the supply-side or the customer-side, or both. We need data to estimate the direct value that the government can withhold, either as funding or as purchase orders. In addition, we also need data and a line-of-thought on whether the government can exert overt influences over other suppliers or customers; this is the indirect value that the government can withhold.
Policy Risk
To unpack policy risk, the organisation needs to be able to accurately classify its DEI policies and activities as affirmative actions, anti-discriminatory actions, or business strategy. Arguably, only affirmative action is the subject of the anti-DEI executive order. If a DEI activity is actually focusing on busting internal anti-discriminatory actions, for example closing the gender pay gap, then the organisation is simply pursuing business optimisation as there will be internal metrics to support the business case of removing these discriminatory obstacles. In a number of cases, what may be seen by the market as affirmative actions may, in fact, be re-couched as business strategy. For example, it is well known that demographic representation is critical if we want to successfully penetrate a demographic-based market segment; for example, hiring women bankers to run the micro-lending division where the target customer is predominantly women.
Internal Risk
Internal risk refers to the impact to employee engagement and talent management resulting from the decision to comply with the anti-DEI executive order. We need to ask questions such as: “Will key talent leave or cheer?” or “Will the organisation have difficulty, or find it easier in attracting key talent going forward?” I would think that any organisation with significant DEI policies and activities would already have data on to be able to estimate this. For example, the organisation would likely have data on how DEI policies and activities are perceived by key talent groups, or pre- / post-data on how the introduction of a DEI policy or activity has changed the behaviour of key talent groups.
Community Risk
Community risk refers to how your customers, supply & distribution partners, and influence groups perceive the importance of your DEI advocacy to your brand positioning. You need to develop an in-depth understanding of the community in which your brand / product operates. For example, Target’s customer base is younger and more upwardly mobile than say Walmart’s, implying that DEI advocacy very likely resonate more with Target’s customers. But understanding your customer base is the easiest part. The blindspot is in evaluating your supply and distribution chain, and whether these partners care about your DEI advocacy position. Consider the case of bartenders (part of the distribution chain) boycotting Jack Daniel’s (Tennessee whiskey) when the brand pulled back its support for Blacks and LGBTQ programmes, which resulted in end-consumers drinking much less Jack Daniel’s at the bars. Similarly, you need to understand the various spheres of community influencers that have evolved with your brand, and their indirect but significant ability to shape support for it.
Conclusion
Utilising this DEI risk framework requires organisations to have comprehensive and well thought through metrics. I have written before about applying the framework of Input → Activity → Output → Outcome → Impact to DEI measurements recently. Hopefully, this DEI risk framework throws up aspects of the domain that business and DEI leaders have not considered before, and this would inspire them to start collecting data and measuring it.
